In traditional data warehousing and Data Mart (DM) environments, data is stored centrally on an External Storage System (ESS), such as, for example, a Storage Area Network (SAN), or locally. A single access point is typically configured in order to provide security (e.g., an ESS) or performance (e.g., access locally), but usually is not able to provide both economically. While an ESS can guarantee security, it may be prohibitively expensive to also provide performance in situations involving high data volumes or IO intensive applications. Conversely, local storage systems typically have high data throughput capabilities, but are not able to store high data volumes effectively or guarantee security without sacrificing storage capacity through excessive redundancy.
Parallel warehousing and DM environments present both opportunities and additional overhead in environments that rely on single storage configurations. Shared-nothing parallel database systems relying on local storage must develop sophisticated solutions for failover recovery (FR) and disaster recovery (DR). Such systems can double or quadruple storage requirements, hence reduce capacity on each server, which can lead to a proliferation of servers or reduced system capacity. Shared-storage parallel database systems (e.g., implementing an ESS) typically rely on centralized high-availability and security services, which reduces the FR and DR infrastructure complexity of parallel solutions, but at the cost of reduced data throughput. This may lead to inefficient use of the parallel systems, limit the expansion capabilities of the system, significantly reduce the system's ability to scale linearly to support increasing data volumes and application demands from expanded user requirements, and/or other drawbacks.